Oral Administration of Compound Probiotics Improved Canine Feed Intake, Weight Gain, Immunity and Intestinal Microbiota

Abstract

Probiotics have been used successfully to promote human and animal health, but only limited studies have focused on using probiotics to improve the health of hosts of different age. Canine microbiome studies may be predictive of results in humans because of the high structural and functional similarity between dog and human microbiomes. A total of 90 dogs were divided into three groups based on dog age (elderly group, n = 30; young group, n = 24; and training group, n = 36). Each group was subdivided into two subgroups, with and without receiving daily probiotic feed additive. The probiotic feed additive contained three different bacterial strains, namely Lactobacillus casei Zhang, Lactobacillus plantarum P-8, and Bifdobacterium animalis subsp. lactis V9. Serum and fecal samples were collected and analyzed at four different time points, i.e., days 0, 30, and 60 of probiotic treatment, and 15 days after ceasing probiotic treatment. The results demonstrated that probiotics significantly promoted the average daily feed intake of the elderly dogs (P < 0.01) and the average daily weight gain of all dogs (P < 0.05), enhanced the level of serum IgG (P < 0.001), IFN-α (P < 0.05), and fecal SIgA (P < 0.001), while reduced the TNF-α (P < 0.05). Additionally, probiotics could change the gut microbial structure of elderly dogs and significantly increased beneficial bacteria (including some Lactobacillus species and Faecalibacterium prausnitzii) and decreased potentially harmful bacteria (including Escherichia coli and Sutterella stercoricanisin), and the elderly dogs showed the strongest response to the probiotics; the relative abundance of some of these species correlated with certain immune factors and physiological parameters, suggesting that the probiotic treatment improved the host health and enhanced the host immunity by stimulating antibody and cytokine secretion through regulating canine gut microbiota. Furthermore, the gut microbiota of the elderly dogs shifted toward a younger-like composition at day 60 of probiotic treatment. Our findings suggested that the probiotic treatment effects on canine health and immunity were age-related and have provided interesting insights into future development of probiotic-based strategies to improve animal and human health.

Keywords: canine; health; immunity; intestinal microbiota; probiotics.

Figure 1

The effects of the probiotics compound administration on host health. Respiration rate (A), body temperature (B), pulse rate (C) of three sample groups (elderly, young, and training groups); (D) the proportion of dogs with diarrhea at day 0 and day 60 of probiotic administration; (E) Changes in severity of diarrhea of the control and probiotic treatment groups. Error bars represent SEM. ^“***” represents comparison between different time points of the treatment group, ^***P < 0.001; “#” represents comparison between the probiotic treatment and control groups at the same time point #P < 0.05. The changes in average daily (F) feed intake and (G) weight gain of the elderly, young, and training dogs, with or without probiotic treatment. Parameters were monitored at days 0, 30, 60, and 15(AC) (15 days after ceasing probiotic treatment). ^*P < 0.05, ^**P < 0.01, and ^***P < 0.001.

Figure 2

Effects of probiotic treatment on canine immune markers. The levels of (A) serum IFN-α, (B) serum IgG, (C) fecal secretory IgA (SIgA), (D) serum IL-6, and (E) serum TNF-α of the elderly, young, and training dogs, with or without probiotic treatment. Parameters were monitored at days 0, 30, 60, and 15(AC) (15 days after ceasing probiotic treatment). ^*P < 0.05, ^**P < 0.01, and ^***P < 0.001.

Figure 3

Difference in different age canine gut microbiota. (A) Contribution of canine breed, gender, severity of diarrhea, and animal age on the gut microbiota composition by permutational multivariate analysis of variance (PERMANOVA) based on the weighted Unifrac distance. Results for both within- and between-group variation are shown. (B) The principal coordinate analysis (PCoA) score plots of all canine subjects based on the weighted UniFrac distance. (C) Bar plots and cladograms showing differential abundant bacteria between the elderly dogs, young dogs, and training dogs, as identified by linear discriminant analysis (LDA) effect size (LEfSe). The LDA cut-off score was 4.

Figure 4

Effect of probiotic application on the gut microbiota diversity and structure. (A) The effect of probiotics on canine gut microbial α diversity. The principal coordinate analysis (PCoA) score plots of the elderly (B), young (C), and training (D) groups based on the weighted UniFrac distance days 0, 30, 60, and 15(AC) (15 days after ceasing probiotic treatment), respectively. F-value and P-value on the PCoA score plots represent the difference of two groups generated by PERMANOVA.

Figure 5

Effects of probiotic administration on the gut microbial composition. The average relative abundance of the differentially bacterial genus in elderly dogs (A) and (B) training dogs, respectively. Heatmaps of differential abundant bacterial species (significantly altered in relative abundance at least at one time point) modulated by probiotic treatment in the (C) elderly, (D) training, and (E) young group. ^*P < 0.05, ^***P < 0.01, and ^***P < 0.001.

Figure 6

Effects of probiotic administration on the gut microbiota age index and composition. (A) Cross-validation error as a function of the number of input genus-level taxa used to regress against the age of dogs, in the order of variable importance. (B) The 20 top ranking age-discriminatory bacterial marker genera as identified by the Random Forests regression. (C) The age index of the gut microbiota of the probiotic-receiving elderly dogs at days 0, 30, 60, and 15(AC) (15 days after ceasing probiotic treatment) (linked by the blue line), as predicted by the Random Forests model. Each dot represents the gut microbiota age index of one dog subject. The brown lines mark the basal levels (at day 0) of the gut microbiota age index of the elderly, young, and training groups.

Figure 7

Network plot showing Spearman's correlation between the differentially bacteria and feed intake, body weight, immune indexes in (A) elderly dogs, (B) training dogs and (C) young dogs. Blue circles and red diamonds represent significantly correlated bacteria and physiological indices (feed intake, body weight, fecal secretory IgA, serum IgG, IFN-α, IL-6, and TNF-α), respectively. The size of the red diamonds corresponds to the number of significantly correlated bacteria. Significant correlations between the bacteria and immune factors, feed intake, body weight are connected by curve; the color of the curve lines represents the correlation strength as illustrated by the color scheme. The color scheme representing the Spearman's rho ranks between 0.6 and −0.6. Positive and negative Spearman's rho represent positive and negative correlation, respectively.